Paper Info
Title
Material specific multiple observation resolution enhancement of hyperspectral imagery
Abstract
In [1] we proposed a hyperspectral imaging model that represents spectral observations at different wavelengths as weighted linear combinations of a small number of basis images obtained through principal component analysis (PCA). Based on this imaging model we formulated a multiple observation resolution enhancement method for hyperspectral imagery. In this work we focus on material specific resolution enhancement. We start with pointing out a shortcoming of the PCA based imaging model. We then introduce modifications to integrate linear spectral mixing into the imaging model. Based on the updated model we introduce and implement material specific multiple observation resolution enhancement for hyperspectral imagery. We test the proposed method on AVIRIS data, and present numeric and visual results.
Year
DOI
Venue
2008
10.1109/ICASSP.2008.4517742
ICASSP
Keywords
Field
DocType
image enhancement,image resolution,principal component analysis,AVIRIS data,hyperspectral imagery,linear spectral mixing,material specific multiple observation resolution enhancement,multiple observation resolution enhancement method,principal component analysis,Hyperspectral imagery,endmember,linear mixing,spatial resolution enhancement
Computer vision,Linear combination,Endmember,Full spectral imaging,Pattern recognition,Computer science,Hyperspectral imaging,Artificial intelligence,Image resolution,Principal component analysis
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Toygar Akgun1909.39
Yucel Altunbasak21507116.78